Development of semi-automated steady state exogenous contrast cerebral blood volume mapping
By: Frank Anthony Provenzano
Publisher: [publisher not identified]
Published: 2016
Place of publication: [New York, N.Y.?]
Language: eng
Description: Functional magnetic resonance imaging (fMRI) as it exists, in its many forms and vari- ants, has revolutionized the fields of neurology and psychology by revealing functional differences non-invasively. Although blood oxygenation level dependent (BOLD) fMRI is used interchangeably with fMRI, it measures one single difference in a phys- iological measurement using a set sequence. As such, there are other established changes in the brain that relate to blood movement and capacity that can also be measured using MRI. One measure, exogenous steady state cerebral blood volume, uses a bolus routine contrast agent administered intravenously alongside a pair of high resolution ‘structural-like’ MRI images to provide detailed information within small cortical and subcortical structures. In this thesis I design a semi-automated algorithm to generate maps of steady state exogenous cerebral blood volume magnetic resonance imaging datasets. To do this I developed an algorithm and tested it on existing MRI scanning protocols.

A series of automated pre-processing steps are developed and tested, including automated scan flagging for artifacts and requisite vascular segmentation. Then, a methodology is developed to create cerebral blood volume (CBV) region of interest (ROI) masks that can then be applied on an existing database to test known CBV dysfunction in a group of patients at high risk for psychosis. Finally, we develop an experiment to see if template based cerebral blood alterations co-registered with class segmentation maps have any positive predictive value in determining disease state in a well characterized cohort of five age-matched groups in an Alzheimer’s disease neuroimaging study.
Notes: Department: Biomedical Engineering.

Thesis advisor: Andrew F. Laine.

Thesis (Ph.D.)--Columbia University, 2016.